CN112630225A - Optical detector based on image defect difference elimination method - Google Patents
Optical detector based on image defect difference elimination method Download PDFInfo
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Abstract
The invention discloses an optical detector based on an image defect difference elimination method, which comprises the following steps: the detection device comprises a base, wherein the upper surface of the base is divided into an operation surface and a detection surface, the operation surface and the detection surface are both provided with openings, a detection box body is arranged on the detection surface, a detection light source is arranged in the detection box body and is aligned with the opening of the detection surface, a track is arranged in the base and extends from the opening of the operation surface to the opening of the detection surface, and a loading platform is arranged on the track; the detection box body is also internally provided with a device for detecting image defect difference, and the device comprises an image data acquisition module, an original illumination image data acquisition module and an image denoising module. The image defect difference elimination method is also disclosed. The original brightness image data are filtered to generate illumination image data, brightening and denoising are integrated in the same frame, the whole processing flow is optimized, the illumination image data can be reused in brightening and denoising, partial operations are combined and simplified, the calculation amount is reduced, and occupied calculation resources are reduced.
Description
Technical Field
The invention relates to the field of optical detection and image processing, in particular to an optical detector based on an image defect difference elimination method.
Background
The optical detection instrument in the prior art cannot identify the difference between the slight flaw and the working flaw, so that misjudgment is easily formed, the working efficiency is low, the intensity of the detected searchlight light is the same intensity, and the limitation and the error rate are high; meanwhile, the existing image denoising processing mode is to denoise and brighten firstly, the two steps are relatively independent processes, the calculation complexity is overlarge, the performance overhead is high, and the image denoising processing mode is difficult to deploy in industrial activities requiring low cost and high efficiency.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects of the background art, the invention discloses an optical detector based on an image defect difference elimination method; the image defect difference elimination method is also disclosed, and the brightening and denoising are integrated in the same frame, so that the whole processing flow is optimized, the calculation amount is greatly reduced, the occupied calculation resources are reduced, and the cost is reduced; and an image noise induction module is added, so that the error correction efficiency and accuracy of the PCB are improved.
The technical scheme is as follows: the invention discloses an optical detector based on an image defect difference elimination method, which comprises the following steps: the detection device comprises a base, wherein the upper surface of the base is divided into an operation surface and a detection surface, the operation surface and the detection surface are both provided with openings, a detection box body is arranged on the detection surface, a detection light source is arranged in the detection box body and is aligned with the opening of the detection surface, a track is arranged in the base and extends from the opening of the operation surface to the opening of the detection surface, and a loading platform for placing a product to be detected is arranged on the track;
the detection box body is also internally provided with a device for detecting image defect difference, and the device comprises an image data acquisition module, an original illumination image data acquisition module and an image denoising module.
Further, the carrying platform is driven by a servo motor to be arranged on the track; the carrying platform is provided with a clamp, and the clamp is arranged on the carrying platform through a rotary machine platform; and a control key for controlling the servo motor and the rotary machine to work is arranged on the operating surface.
Furthermore, a light intensity control module is arranged in the detection box body and connected with the detection light source, the light intensity control module is a photosensitive resistor, and the brightness of the detection light source is automatically adjusted according to the brightness of the surrounding environment.
Furthermore, a system host for controlling the whole equipment, a display screen and a keyboard which are connected with the system host are also arranged in the base.
Also disclosed is an image defect difference elimination method, which is realized based on the device for detecting image defect difference and comprises the following steps:
s1, acquiring original image data, wherein the original image data comprises original brightness image data representing brightness;
s2, under the condition of reducing contrast, filtering the original brightness image data to obtain original illumination image data;
s3, improving the brightness of the original illumination image data to obtain target illumination image data;
s4, if the original image data is the superposition of the original illumination image data and the reflection image data, synthesizing the target illumination image data and the reflection image data into characteristic image data;
and S5, de-noising the characteristic image data by referring to the target illumination image data to obtain target image data.
In S1, if image data is generated in an environment with ultra-dark light and the brightness of the image data is low, acquiring the image data to wait for brightening and denoising, where the image data is original image data; wherein the environment with ultra-dark light refers to the environment with light rays at 0-501 ux.
Further, in S2, a convex curve expressed as a function is preset, and the original luminance image data is converted from the real number domain to the target domain by the preset convex curve to reduce the contrast; the brightness value of each pixel point in the original brightness image data is in a real number domain, the brightness value of each pixel point in the original brightness image data is input into a function represented by the convex curve, and the brightness value after mapping is output, so that the brightness value of each pixel point in the original brightness image data is mapped to another domain from the real number domain to serve as a target domain; for the convex curve, the part of the brightness value of each pixel point in the brightness image data mapped to the middle is more concentrated, the part of the brightness value of each pixel point in the original brightness image data mapped to the two sides is sparser, and for the original brightness image data, the high gray level and the low gray level are distinguished, so that the contrast of the brightness is reduced;
the target domain comprises a logarithmic domain, and the luminance value of each pixel point in the original luminance image data is subjected to logarithmic conversion so as to convert the original luminance image data from a real number domain to the logarithmic domain; the logarithmic transformation expands the low gray value part of the original brightness image data to display more details of the low gray value part, compresses the high gray value part of the original brightness image data to reduce the details of the high gray value part, so as to emphasize the low gray value part in the brightness image data, and the logarithmic transformation enhances the details of the low gray value part in the original brightness image data.
Further, in S3, gamma correction is performed on the original illumination image data to increase the brightness value of each pixel in the original illumination image data, which is used as the target illumination image data.
Further, in S4, the target illumination image data after the brightness is increased and the reflection image data are combined to obtain the characteristic image data, and the process is expressed as follows:
I"＝I^γ
L'＝I"·T
where I ^ γ denotes that the original illumination image data I is brightened, I "denotes target illumination image data after the brightness is brightened, T denotes reflection image data, and L' denotes feature image data.
Has the advantages that: compared with the prior art, the invention has the advantages that: the original brightness image data are filtered to generate illumination image data, brightening and denoising are integrated in the same frame, the whole processing flow is optimized, the illumination image data can be reused in brightening and denoising, partial operations are combined and simplified, the calculation amount is greatly reduced, occupied calculation resources are reduced, the calculation speed can be guaranteed, the brightening and denoising performance and the image data quality are both considered, the workload is reduced, the working efficiency is improved, and the cost is reduced; secondly, this instrument is conveniently got and is put to realize adopting the light of different intensity to detect the product through light intensity control module, improve the detection accuracy of product.
Drawings
FIG. 1 is a side cross-sectional view of the optical inspection apparatus of the present invention;
FIG. 2 is a top view of the optical inspection apparatus of the present invention;
FIG. 3 is a flowchart of an image defect difference elimination method according to the present invention.
Detailed Description
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
The optical detector shown in fig. 1 and 2 includes: the detection device comprises a base 1, the upper surface of the base 1 is divided into an operation surface 101 and a detection surface 102, the operation surface 101 and the detection surface 102 are both open, the detection surface 102 is provided with a detection box body 2, a detection light source 3 is arranged in the detection box body 2 and is aligned to the opening of the detection surface 102, a track 4 is arranged in the base 1 and extends to the opening of the detection surface 102 from the opening of the operation surface 101, a carrying platform 5 for placing a product to be detected is arranged on the track 4, the product to be detected is placed on the carrying platform 5 from the opening of the operation surface 101 and is conveyed to the position below the detection light source 3 through a guide rail.
The detection box body 2 is also internally provided with a device 6 for detecting image defect difference, which comprises an image data acquisition module, an original illumination image data acquisition module and an image denoising module. And de-noising the characteristic image data by referring to the target illumination image data acquired by the detection light source to acquire target image data.
The carrying platform 5 is arranged on the track 4 through the driving of the servo motor 7, a clamp 8 is arranged on the carrying platform 5, the clamp 8 is arranged on the carrying platform 5 through the rotary machine table, and a control key 9 for controlling the servo motor 7 and the rotary machine table to work is arranged on the operating surface 101.
The light intensity control module 10 is arranged in the detection box body 2 and connected with the detection light source 3, the light intensity control module 10 adopts a photosensitive resistor, and the brightness of the detection light source 3 is automatically adjusted according to the brightness of the surrounding environment.
A system host for controlling the whole equipment, a display screen 11 and a keyboard 12 connected with the system host are also arranged in the base 1.
The instrument has the following working procedures: starting drive, to wait to detect the product and place on microscope carrier 5, anchor clamps 8 are tight, microscope carrier 5 moves the below of detecting light source 3 along the guide rail, open the detection power, the revolving stage has the angle adjustment wheel, the angle of product is waited to detect in the control, light intensity control module 10 carries out the light intensity response to the detecting light source, and automatic control searchlight light source is in order to adapt to ambient light intensity, it detects the product to treat to detect to use different light intensity, system detects image defect difference device 6 and detects automatically, mark and the adjustment to the defect that the module discerned and the wrong dress of post, transmit for display screen 11 through the system's host computer.
As shown in fig. 3, an image defect difference eliminating method includes the following steps:
and S1, acquiring original image data, and in practical application, if image data is generated in an environment with ultra-dark light and the brightness of the image data is low, acquiring the image data to wait for brightening and denoising, wherein the image data can be called as original image data. By ultra-dim environment, it is meant an environment with little light (e.g., 0-501ux), such as outdoors at night, in a room with poor light transmission, etc.
And S2, under the condition of reducing the contrast of the brightness, filtering the original brightness image data so as to keep edges and reduce noise and smooth, wherein the image data after the filtering processing is the original illumination image data in the Retina model.
The contrast of the luminance is reduced in order to perform the filtering process on the original luminance image data, and the contrast of the luminance is restored after the filtering process on the original luminance image data is completed.
In this embodiment, a convex curve may be preset, and the convex curve may be expressed by a function, such as a logarithmic function, a polynomial function, and the like. And converting the original brightness image data from a real number domain to a target domain through a preset convex curve so as to reduce the contrast.
And the brightness value of each pixel point in the original brightness image data is in a real number domain, the brightness value of each pixel point in the original brightness image data is input into the function represented by the convex curve, and the brightness value after mapping is output, so that the brightness value of each pixel point in the original brightness image data is mapped to another domain from the real number domain to serve as a target domain.
For the convex curve, the part of the brightness value of each pixel point in the brightness image data mapped to the middle part can be concentrated, and the part of the brightness value of each pixel point in the original brightness image data mapped to the two sides can be sparse, so that for the original brightness image data, the high gray level and the low gray level can be distinguished, and the contrast of the brightness is reduced.
The target domain comprises a logarithmic domain, and the luminance value of each pixel point in the original luminance image data can be subjected to logarithmic conversion so as to convert the original luminance image data from a real number domain to the logarithmic domain.
The logarithmic conversion can expand the low gray value part of the original luminance image data, display more details of the low gray value part, compress the high gray value part of the original luminance image data, and reduce the details of the high gray value part, thereby emphasizing the low gray value part in the luminance image data. The logarithmic transformation enhances the detail of the low gray scale part in the original brightness image data.
And S3, improving the brightness of the original illumination image data to obtain target illumination image data.
In this embodiment, gamma correction is performed on the original illumination image data to increase the brightness value of each pixel point in the original illumination image data, which is used as the target illumination image data. The image data after the gamma correction is target illumination image data.
And S4, if the original image data is the superposition between the original illumination image data and the reflection image data, synthesizing the target illumination image data and the reflection image data into characteristic image data.
Among them, in Retinex theory, image data obtained by human eyes depends on incident light and reflection of incident light by an object surface. The image data is first illuminated by the incident light and reflected by the object into the imaging system to form what is seen. In this process, the reflectivity is determined by the object itself, is not affected by the incident light, and can be expressed by the following formula:
L＝I·T
where L represents raw image data received by an observed or camera, T represents an illumination component of ambient light, i.e., illumination image data, and T represents a reflection component of a target object carrying image detail information, i.e., reflection image data. And (3) resolving the reflection image data from the original image data by referring to the original illumination image data, namely, abandoning the property of the original illumination image data I from the original image data L, thereby separating the original appearance of the object, namely, the reflection image data T and eliminating the influence of uneven illumination.
In this embodiment, the target illumination image data after the brightness is increased and the reflection image data are synthesized to obtain the characteristic image data, and this process is expressed as follows:
I"＝I^γ
L'＝I"·T
where I ^ γ denotes that the original illumination image data I is brightened, I "denotes target illumination image data after the brightness is brightened, T denotes reflection image data, and L' denotes feature image data.
And S5, referring to the target illumination image data, and denoising the characteristic image data to obtain target image data.
Claims (9)
1. An optical detector based on an image defect difference elimination method is characterized by comprising the following steps: the detection device comprises a base (1), wherein the upper surface of the base (1) is divided into an operation surface (101) and a detection surface (102), the operation surface (101) and the detection surface (102) are both provided with openings, a detection box body (2) is arranged on the detection surface (102), a detection light source (3) is arranged in the detection box body (2) and is aligned to the opening of the detection surface (102), a track (4) is arranged in the base (1) and extends from the opening of the operation surface (101) to the opening of the detection surface (102), and a carrying platform (5) for placing a product to be detected is arranged on the track (4);
the detection box body (2) is also internally provided with a device (6) for detecting image defect difference, and the device comprises an image data acquisition module, an original illumination image data acquisition module and an image denoising module.
2. The optical detector based on the image defect difference elimination method of claim 1, characterized in that: the carrying platform (5) is arranged on the track (4) in a driving way through a servo motor (7); a clamp (8) is arranged on the carrying platform (5), and the clamp (8) is arranged on the carrying platform (5) through a rotating machine platform; and a control key (9) for controlling the servo motor (7) and the rotary machine table to work is arranged on the operating surface (101).
3. The optical detector based on the image defect difference elimination method of claim 1, characterized in that: the light intensity control module (10) is arranged in the detection box body (2) and is connected with the detection light source (3), the light intensity control module (10) is a photosensitive resistor, and the brightness of the detection light source (3) is automatically adjusted according to the brightness of the surrounding environment.
4. The optical detector based on the image defect difference elimination method of claim 1, characterized in that: and a system host used for controlling the whole equipment, a display screen (11) and a keyboard (12) connected with the system host are also arranged in the base (1).
5. An image defect difference elimination method, implemented based on said means (6) for detecting image defect differences, comprising the steps of:
s1, acquiring original image data, wherein the original image data comprises original brightness image data representing brightness;
s2, under the condition of reducing contrast, filtering the original brightness image data to obtain original illumination image data;
s3, improving the brightness of the original illumination image data to obtain target illumination image data;
s4, if the original image data is the superposition of the original illumination image data and the reflection image data, synthesizing the target illumination image data and the reflection image data into characteristic image data;
and S5, de-noising the characteristic image data by referring to the target illumination image data to obtain target image data.
6. The image defect difference elimination method according to claim 5, characterized in that: in S1, if image data is generated in an environment with ultra-dark light and the brightness of the image data is low, acquiring the image data to wait for brightening and denoising, where the image data is original image data; wherein the environment with ultra-dark light refers to the environment with light rays at 0-501 ux.
7. The image defect difference elimination method according to claim 5, characterized in that: in S2, a convex curve expressed by a function is preset, and the original luminance image data is converted from a real number domain to a target domain by the preset convex curve to reduce contrast; the brightness value of each pixel point in the original brightness image data is in a real number domain, the brightness value of each pixel point in the original brightness image data is input into a function represented by the convex curve, and the brightness value after mapping is output, so that the brightness value of each pixel point in the original brightness image data is mapped to another domain from the real number domain to serve as a target domain; for the convex curve, the part of the brightness value of each pixel point in the brightness image data mapped to the middle is more concentrated, the part of the brightness value of each pixel point in the original brightness image data mapped to the two sides is sparser, and for the original brightness image data, the high gray level and the low gray level are distinguished, so that the contrast of the brightness is reduced;
the target domain comprises a logarithmic domain, and the luminance value of each pixel point in the original luminance image data is subjected to logarithmic conversion so as to convert the original luminance image data from a real number domain to the logarithmic domain; the logarithmic transformation expands the low gray value part of the original brightness image data to display more details of the low gray value part, compresses the high gray value part of the original brightness image data to reduce the details of the high gray value part, so as to emphasize the low gray value part in the brightness image data, and the logarithmic transformation enhances the details of the low gray value part in the original brightness image data.
8. The image defect difference elimination method according to claim 5, characterized in that: in S3, gamma correction is performed on the original illumination image data to increase the brightness value of each pixel in the original illumination image data, which is used as the target illumination image data.
9. The image defect difference elimination method according to claim 5, characterized in that: in S4, the target illumination image data after the brightness is increased and the reflection image data are combined to obtain the characteristic image data, and the process is represented as follows:
I"＝I^γ
L'＝I"·T
where I ^ γ denotes that the original illumination image data I is brightened, I "denotes target illumination image data after the brightness is brightened, T denotes reflection image data, and L' denotes feature image data.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN208833676U (en) * | 2018-09-21 | 2019-05-07 | 河源职业技术学院 | A kind of Machine Vision Detection platform |
| CN110018178A (en) * | 2019-04-28 | 2019-07-16 | 华南理工大学 | A kind of mobile phone bend glass typical defect on-line measuring device and method |
| CN111899197A (en) * | 2020-08-05 | 2020-11-06 | 广州市百果园信息技术有限公司 | Image brightening and denoising method and device, mobile terminal and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN208833676U (en) * | 2018-09-21 | 2019-05-07 | 河源职业技术学院 | A kind of Machine Vision Detection platform |
| CN110018178A (en) * | 2019-04-28 | 2019-07-16 | 华南理工大学 | A kind of mobile phone bend glass typical defect on-line measuring device and method |
| CN111899197A (en) * | 2020-08-05 | 2020-11-06 | 广州市百果园信息技术有限公司 | Image brightening and denoising method and device, mobile terminal and storage medium |
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